Artificial Intelligence and Machine Learning

Define Artificial Intelligence. Discuss the scope of AI.

Differentiate Forward Chaining and Backward Chaining.

List the different types of Expert Systems with examples.

Discuss about Knowledge acquisition process and meta knowledge in expert system.

Define Constraint Satisfaction Problem with an example.

What is Beam Search? Explain its working.

Describe unification with an example.

Compare supervised, unsupervised, and reinforcement learning.

Describe different types of intelligent agents with suitable examples and diagrams.

Explain knowledge-based systems in detail. Discuss knowledge representation, inference mechanisms, and applications.

Explain the architecture of an expert system in detail. Illustrate all components with a neat diagram.

Discuss any two uninformed search methods with examples.

Analyze AO* algorithm with a suitable example. State the limitations in the algorithm.

Construct alpha-beta pruning algorithm and the Minmax game playing algorithm with examples.

Illustrate PROLOG representation and program structure with examples of facts, rules, and queries.

Analyze decision tree learning, including entropy and information gain with suitable examples.

Construct and implement Naive Bayes and Support Vector Machine to classify the IRIS dataset.

Describe K-Means and hierarchical clustering algorithm in detail. Compare their approaches and applications.